Black format check --- /home/runner/work/flair/flair/flair/data.py 2024-01-26 17:36:58.540049+00:00 +++ /home/runner/work/flair/flair/flair/data.py 2024-01-26 17:39:09.462028+00:00 @@ -1042,16 +1042,14 @@ def get_span(self, start: int, stop: int): span_slice = slice(start, stop) return self[span_slice] @typing.overload - def __getitem__(self, idx: int) -> Token: - ... + def __getitem__(self, idx: int) -> Token: ... @typing.overload - def __getitem__(self, s: slice) -> Span: - ... + def __getitem__(self, s: slice) -> Span: ... def __getitem__(self, subscript): if isinstance(subscript, slice): return Span(self.tokens[subscript]) else:

Black format check --- /home/runner/work/flair/flair/flair/file_utils.py 2024-01-26 17:36:58.548049+00:00 +++ /home/runner/work/flair/flair/flair/file_utils.py 2024-01-26 17:39:09.992706+00:00 @@ -1,6 +1,7 @@ """Utilities for working with the local dataset cache. Copied from AllenNLP.""" + import base64 import functools import io import logging import mmap

Black format check --- /home/runner/work/flair/flair/flair/models/entity_linker_model.py 2024-01-26 17:36:58.548049+00:00 +++ /home/runner/work/flair/flair/flair/models/entity_linker_model.py 2024-01-26 17:39:19.389890+00:00 @@ -106,13 +106,13 @@ **classifierargs: The arguments propagated to :meth:`flair.nn.DefaultClassifier.__init__` """ super().__init__( embeddings=embeddings, label_dictionary=label_dictionary, - final_embedding_size=embeddings.embedding_length * 2 - if pooling_operation == "first_last" - else embeddings.embedding_length, + final_embedding_size=( + embeddings.embedding_length * 2 if pooling_operation == "first_last" else embeddings.embedding_length + ), **classifierargs, ) self.pooling_operation = pooling_operation self._label_type = label_type

Black format check --- /home/runner/work/flair/flair/flair/models/entity_mention_linking.py 2024-01-26 17:36:58.548049+00:00 +++ /home/runner/work/flair/flair/flair/models/entity_mention_linking.py 2024-01-26 17:39:20.069283+00:00 @@ -445,13 +445,15 @@ @classmethod def _from_state(cls, state_dict: Dict[str, Any]) -> "EntityPreprocessor": return cls( ab3p_path=Path(state_dict["ab3p_path"]), word_data_dir=Path(state_dict["word_data_dir"]), - preprocessor=None - if state_dict["preprocessor"] is None - else EntityPreprocessor._from_state(state_dict["preprocessor"]), + preprocessor=( + None + if state_dict["preprocessor"] is None + else EntityPreprocessor._from_state(state_dict["preprocessor"]) + ), ) class CandidateSearchIndex(ABC): """Base class for a candidate generator. @@ -898,13 +900,15 @@ # Preprocess entity mentions for entity in entities_mentions: data_points.append(entity.data_point) mentions.append( - self.preprocessor.process_mention(entity.data_point.text, sentence) - if self.preprocessor is not None - else entity.data_point.text, + ( + self.preprocessor.process_mention(entity.data_point.text, sentence) + if self.preprocessor is not None + else entity.data_point.text + ), ) # Retrieve top-k concept / entity candidates for i in range(0, len(mentions), batch_size): candidates = self.candidate_generator.search(entity_mentions=mentions[i : i + batch_size], top_k=top_k)

ruff pytest_ruff.RuffError: flair/models/entity_mention_linking.py:1163:24: C401 Unnecessary generator (rewrite as a `set` comprehension) | 1161 | spans = sentence.get_spans(gold_label_type) 1162 | for span in spans: 1163 | exps = set(exp.value for exp in span.get_labels(gold_label_type) if exp.value not in exclude_labels) | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ C401 1164 | 1165 | predictions = set(pred.value for pred in span.get_labels("predicted")) | = help: Rewrite as a `set` comprehension flair/models/entity_mention_linking.py:1165:31: C401 Unnecessary generator (rewrite as a `set` comprehension) | 1163 | exps = set(exp.value for exp in span.get_labels(gold_label_type) if exp.value not in exclude_labels) 1164 | 1165 | predictions = set(pred.value for pred in span.get_labels("predicted")) | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ C401 1166 | total += 1 1167 | if exps & predictions: | = help: Rewrite as a `set` comprehension

Black format check --- /home/runner/work/flair/flair/flair/nn/model.py 2024-01-26 17:36:58.548049+00:00 +++ /home/runner/work/flair/flair/flair/nn/model.py 2024-01-26 17:39:25.475635+00:00 @@ -699,13 +699,15 @@ device=flair.device, ) else: return torch.tensor( [ - self.label_dictionary.get_idx_for_item(label[0]) - if len(label) > 0 - else self.label_dictionary.get_idx_for_item("O") + ( + self.label_dictionary.get_idx_for_item(label[0]) + if len(label) > 0 + else self.label_dictionary.get_idx_for_item("O") + ) for label in labels ], dtype=torch.long, device=flair.device, )

Black format check --- /home/runner/work/flair/flair/flair/nn/distance/euclidean.py 2024-01-26 17:36:58.548049+00:00 +++ /home/runner/work/flair/flair/flair/nn/distance/euclidean.py 2024-01-26 17:39:26.583775+00:00 @@ -14,11 +14,10 @@ It was published under MIT License: https://github.com/asappresearch/dynamic-classification/blob/master/LICENSE.md Source: https://github.com/asappresearch/dynamic-classification/blob/55beb5a48406c187674bea40487c011e8fa45aab/distance/euclidean.py """ - import torch from torch import Tensor, nn

test_bel_dictionary[False] ValueError: When loading a custom dictionary, you need to specify a dataset_name!

test_train_tars[False] OSError: Unable to load weights from pytorch checkpoint file for './cache/transformers/hub/models--sshleifer--tiny-distilroberta-base/snapshots/d305c58110158c865cb6746c62d4511d4148a934/pytorch_model.bin' at './cache/transformers/hub/models--sshleifer--tiny-distilroberta-base/snapshots/d305c58110158c865cb6746c62d4511d4148a934/pytorch_model.bin'. If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True.

TestTransformerWordEmbeddings.test_layoutlm_embeddings[False] OSError: Unable to load weights from pytorch checkpoint file for './cache/transformers/hub/models--microsoft--layoutlm-base-uncased/snapshots/8290fe08a848303616911d513e66ec192840ffbd/pytorch_model.bin' at './cache/transformers/hub/models--microsoft--layoutlm-base-uncased/snapshots/8290fe08a848303616911d513e66ec192840ffbd/pytorch_model.bin'. If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True.